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Element Details (right panel)

This part of the designer displays details of the workflow or the selected item:

If nothing is selected, information about the workflow will be shown, such as:

  • Name: the name of the application
  • Description: an optional textual description
  • Tags: keywords useful for categorization
  • Access Level: visibility level (e.g., Team or Public)
  • and the Create Application button to save the application

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When an element is selected within the designer, the right panel updates with the details of that selected block. Below are some examples:

Dataset

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When selecting a dataset in the Data Analytics System designer, the right panel displays structural and functional details about the dataset. This helps users quickly understand its content and data types.

The panel displays the following information:

  • Dataset Name: Displayed at the top in bold, e.g., IRIS

  • Type: Indicates the asset type, in this case: Type: table

  • Data Source: Shows the dataset’s data source. In this example, public means the dataset is stored in a public source accessible to all users logged in Data Analytics System. By clicking on the Datasource name, you will have the datasource details page.

  • Dataset Columns: In case of tabular dataset tabellare, it shows the list of dataset columns, with related types:

    • Name: column name
    • Type: type of data (i.e. Number, String, ecc.)

Finally, using Show Preview button, it's possible to visualize the dataset preview:

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Service

When selecting a Service in the Data Analytics System designer (identified by its salmon color), the right panel displays configuration information and parameters for the microservice.

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This view allows the user to customize service execution and understand its behavior.

  • Advanced configuration panel on the right: When selecting a block (in this case, the SKLEARN-MODEL service), the right panel shows not only general information, but also allows the configuration of internal parameters for the service.

➔ For example, you can see:

  • The classification label name (LabelColumn)
  • The chosen algorithm (RandomForestClassifier)
  • Algorithm parameters (algorithm_params) such as number of trees, max depth, etc.
  • Dataset split parameters (train_test_split_args)

Key Features

  • Flexible parameter types: Service parameters can be of various types (strings, numbers, JSON, etc.) defined by the service creator and are editable directly from the interface without needing external code.
  • Required and optional parameters: The developer can mark which parameters are required and which are optional, guiding the user during configuration.
  • Per-service customization: The Designer allows users to individually configure each block with detailed parameters without leaving the canvas.
  • Instant visual feedback: As soon as a node is selected, its details are shown and editable. No reloading or external popups required.

Note

Service-specific configurable parameters are split into two tabs. Values under the Application tab are passed via args, while those under the STATIC tab are passed as environment variables. This distinction is defined by the service developer when the service is registered.

Model

When selecting a model block (blue color) within the Data Analytics System designer, the right panel shows a set of descriptive and technical details about the model. These help the user understand its origin, the data used to build it, and the application context.

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Visible fields include:

  • Model Name: Displayed at the top in bold. (For example: MODEL CREATED BY AMT – AUTONOMY FORECASTING ANALYTICS). It's accompanied by a short description of the model’s origin, providing helpful context.

  • Algorithm: Indicates the machine learning algorithm used to train the model. In the example: tf-regressor-mlflow (a regression model developed with TensorFlow and tracked with MLflow)

  • Input Columns: List of dataset columns used as inputs in the model.
    In this case: sepal_length, sepal_width, petal_length, and petal_width.

  • Dataset: Shows the name of the dataset used to train the model. Example: dataset iris

Note

Clicking this item lets the user view the associated dataset.

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  • Application / Workflow - ID or reference to the application or workflow where the model was originally created.

Note

This element is clickable so to open the context where the model was generated.

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  • Data Source: Indicates the visibility level of the data source hosting the model.